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How machine learning can improve COVID testing -- GCN

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On June 18, the Food and Drug Administration authorized the use of pooled testing for identifying COVID-19 infections. The method allows up to four swabs to be tested at once – a strategy that is expected to greatly expand frequent testing to larger sections of the population. The idea is that if a bundled sample comes back positive, then all the individuals in that sample will need to be tested separately. If a bundled sample comes back clean, however, that's four people who don't need to be tested further, saving public health officials time and money. The FDA said it expects pooling will allow virus identification with fewer tests, which means more tests could be run at once, fewer testing supplies would be consumed and patients could likely receive results more quickly.


FDA clears CINA Head neurovascular imaging artificial intelligence tool

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Medical imaging artificial intelligence (AI) specialist Avicenna.AI has announced it has received 510(k) clearance from the US Food and Drug Administration (FDA) for its CINA Head triage AI solution for neurovascular emergencies. The FDA's decision covers CINA's automatic detection capabilities for both intracranial haemorrhage and large vessel occlusion (LVO) from CT-scan imaging. Stroke is a leading cause of death in the USA, with more than 795,000 strokes resulting in more than 100,000 deaths each year. It is estimated that up to a third of the most common type of stroke are caused by LVO, when a clot blocks the circulation of the blood in the brain. Around one in 10 strokes are thought to be caused by intracranial haemorrhage.


MIM Software Inc. Receives FDA 510(k) Clearance for Deep Learning

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MIM Software Inc., a leading global provider of medical imaging software, announced it has received 510(k) clearance from the US Food and Drug Administration (FDA) for its deep learning auto-contouring software, Contour ProtégéAI . Contour ProtégéAI is an auto-contouring solution that seamlessly integrates into any department's workflow and can be rapidly implemented into virtually any environment. User feedback and a determination to continuously improve auto-segmentation were key drivers in developing the product. "Our customers are under continual pressure to improve their practices while facing escalating time constraints," said Andrew Nelson, Chief Executive Officer of MIM Software Inc. "Our deep learning auto-segmentation product, Contour ProtégéAI, will play a critical role in reducing the burden of contouring." Auto-contouring is an ideal use case for deep learning algorithms because it is one of the most time-consuming clinical tasks.


MIM Software Inc. Receives FDA 510(k) Clearance for Deep Learning Auto-Contouring Software

#artificialintelligence

MIM Software Inc., a leading global provider of medical imaging software, announced today it has received 510(k) clearance from the U.S. Food and Drug Administration (FDA) for its deep learning auto-contouring software, Contour ProtégéAI . Contour ProtégéAI is an auto-contouring solution that seamlessly integrates into any department's workflow and can be rapidly implemented into virtually any environment. User feedback and a determination to continuously improve auto-segmentation were key drivers in developing the product. "Our customers are under continual pressure to improve their practices while facing escalating time constraints,'' said Andrew Nelson, Chief Executive Officer of MIM Software Inc. "Our deep learning auto-segmentation product, Contour ProtégéAI, will play a critical role in reducing the burden of contouring." Auto-contouring is an ideal use case for deep learning algorithms because it is one of the most time-consuming clinical tasks.


AI Hype and Radiology: A Plea for Realism and Accuracy

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This opinion piece is inspired by the old Danish proverb: "Making predictions is hard, especially about the future" (1). As every reader knows, the momentum of artificial intelligence (AI) and the eventual implementation of deep learning models seem assured. Some pundits have gone considerably further, however, and predicted a sweeping AI takeover of radiology. Although many radiologists support AI and believe it will enable greater efficiency, a recent study of medical students found very different reactions (2). While such doomsday predictions are understandably attention-grabbing, they are highly unlikely, at least in the short term.


Coronavirus Testing: FDA Approves Quicker, Cheaper New Antigen Test

International Business Times

In its first use of emergency authorization, the U.S. Food and Drug Administration (FDA) on Friday approved the production of a new coronavirus antigen test category. These tests will be able to track antigen proteins using naval cavity samples collected with swabs. With these tests, medical professionals can detect the presence of COVID-19 antigens in a matter of minutes. They are also much cheaper to produce than the tests currently in use. While much quicker, these new tests are unable to track as many infections as the standard polymerase chain reaction tests and are more likely to deliver false negatives.


FDA Clears Siemens AIDAN Artificial Intelligence for Biograph PET/CT IAM Network

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April 22, 2020 -- Siemens Healthineers has received clearance from the Food and Drug Administration (FDA) for its AIDAN artificial intelligence technologies on the Biograph family of positron emission tomography/computed tomography (PET/CT) systems, which includes the Biograph Horizon, Biograph mCT, and Biograph Vision. AIDAN is built on a foundation of patient-focused bed design and proprietary AI deep-learning technology to enable four new features – FlowMotion AI, OncoFreeze AI, PET FAST Workflow AI, and Multiparametric PET Suite AI. Siemens Healthineers PET/CT systems with AIDAN offer enhanced protection against cyber threats via syngo Security – a security package for general regulatory security rules that enables compliance with the Health Insurance and Accountability Act (HIPAA). FlowMotion AI Because each patient's body habitus and presentation of disease is different, tailoring PET/CT protocols to produce the highest-quality diagnostic imaging information possible for each patient can be difficult and time-consuming. The standard one-size-fits-all protocol lacks personalization and is often of suboptimal quality.


DeepSIBA: Chemical Structure-based Inference of Biological Alterations

arXiv.org Machine Learning

Predicting whether a chemical structure shares a desired biological effect can have a significant impact for in-silico compound screening in early drug discovery. In this study, we developed a deep learning model where compound structures are represented as graphs and then linked to their biological footprint. To make this complex problem computationally tractable, compound differences were mapped to biological effect alterations using Siamese Graph Convolutional Neural Networks. The proposed model was able to learn new representations from chemical structures and identify structurally dissimilar compounds that affect similar biological processes with high precision. Additionally, by utilizing deep ensembles to estimate uncertainty, we were able to provide reliable and accurate predictions for chemical structures that are very different from the ones used during training. Finally, we present a novel inference approach, where the trained models are used to estimate the signaling pathways affected by a compound perturbation in a specific cell line, using only its chemical structure as input. As a use case, this approach was used to infer signaling pathways affected by FDA-approved anticancer drugs.


How Artificial Intelligence Is Accelerating Life Sciences

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The drug development lifecycle is long and fraught with heavy risk -- it takes a staggering 10 – 15 years on average, with ultimately only 12 percent of drugs in clinical trials gaining approval by the U.S. Food and Drug Administration (FDA) [1]. To put this in perspective, 22.7 percent of all global research and development spending in 2017 was in the healthcare industry, second only to 23.1 percent spent in the computing and electronics industry, yet the product lifecycle is longer, and costs are much higher [2]. For example, the original iPhone took two and a half years to develop from concept to launch, and an estimated $150 million spent in research and development [3]. In contrast, the average cost of new drug and biologics is $2.87 billion when factoring in the post-approval research and development costs, according to figures released in May 2016 by The Tufts Center for the Study of Drug development (CSDD) [4]. For pharmaceutical companies that have launched more than four drugs, the median cost is closer to a staggering $5.3 billion according to analysis by industry expert Matthew Herper of Forbes [5].


Alphabet's Next Billion-Dollar Business: 10 Industries To Watch - CB Insights Research

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Alphabet is using its dominance in the search and advertising spaces -- and its massive size -- to find its next billion-dollar business. From healthcare to smart cities to banking, here are 10 industries the tech giant is targeting. With growing threats from its big tech peers Microsoft, Apple, and Amazon, Alphabet's drive to disrupt has become more urgent than ever before. The conglomerate is leveraging the power of its first moats -- search and advertising -- and its massive scale to find its next billion-dollar businesses. To protect its current profits and grow more broadly, Alphabet is edging its way into industries adjacent to the ones where it has already found success and entering new spaces entirely to find opportunities for disruption. Evidence of Alphabet's efforts is showing up in several major industries. For example, the company is using artificial intelligence to understand the causes of diseases like diabetes and cancer and how to treat them. Those learnings feed into community health projects that serve the public, and also help Alphabet's effort to build smart cities. Elsewhere, Alphabet is using its scale to build a better virtual assistant and own the consumer electronics software layer. It's also leveraging that scale to build a new kind of Google Pay-operated checking account. In this report, we examine how Alphabet and its subsidiaries are currently working to disrupt 10 major industries -- from electronics to healthcare to transportation to banking -- and what else might be on the horizon. Within the world of consumer electronics, Alphabet has already found dominance with one product: Android. Mobile operating system market share globally is controlled by the Linux-based OS that Google acquired in 2005 to fend off Microsoft and Windows Mobile. Today, however, Alphabet's consumer electronics strategy is being driven by its work in artificial intelligence. Google is building some of its own hardware under the Made by Google line -- including the Pixel smartphone, the Chromebook, and the Google Home -- but the company is doing more important work on hardware-agnostic software products like Google Assistant (which is even available on iOS).